A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight

Abstract Effective forecasting of the air pollutant concentration is crucial for a robust air quality early-warning system and has both theoretical and practical significance. However, the accidental and cognitive uncertainty in the model selection or parameter setting of a single system will result in inaccurate and unstable forecasting results. Thus, in this paper, a novel fuzzy combination forecasting system based on the data preprocessing, fuzzy theory, and advanced optimization algorithm is proposed to improve the accuracy and stability of forecasting results. Based on the fuzzy theory and decorrelation maximization method, our proposed forecasting system can considering more information and maintaining the diversity of models. Moreover, Cuckoo Search algorithm applied in the system can determine the optimal weights for models aggregation. Several experiments based on PM 2.5 and PM10 datasets in three cities are analyzed and discussed to verify the excellent performance of our proposed forecasting system, and the results indicate that the forecasting system outperforms others with respect to the accuracy, stability and generalization capabilities which are the basis of a robust air quality early-warning system in practice.

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